Method and system for obtaining a sequence of x-ray images using a reduced dose of ionizing radiation

ABSTRACT

Methods, systems, and apparatus for obtaining a sequence of x-ray images are disclosed. An object of interest in a first x-ray image is detected and an area of interest, based on a predicted motion of the object of interest, is determined. A second x-ray image of the area of interest is acquired using spatial x-ray modification to control an x-ray to pass through a portion of a patient corresponding to the area of interest.

TECHNICAL FIELD

This specification relates generally to systems and methods forobtaining a sequence of x-ray images, and more particularly to systemsand methods for detecting objects in x-ray images.

BACKGROUND

Needle path planning and needle guidance are used in non-invasivesurgical procedures in order to avoid critical structure and reducetissue trauma. During needle path planning DynaCT is used to establishthe needle path, and during needle guidance, real time fluoroscopy isused to guide the needle to a target location. Fluoroscopic images andDynaCT are generated using X-rays which are a form of ionizingradiation. To prevent adverse effects from exposure to ionizingradiation the dose is typically managed by restricting the use of thesemodalities in duration, frequency and space.

Currently, conventional systems achieve a reduction of spatial ionizingradiation dose by allowing for manual alteration of collimator positionsby a user. The user is required to identify a particular region of apatient's body and then manually adjust the collimators so that an x-rayimage of that particular region will be properly acquired. Such anapproach is time consuming, dependent on the user and disruptive to theclinical workflow.

A wide variety of collimators are available, such as collimators withrectangular panels, leaf collimators and semi-transparent collimators.Each type of collimator provides a varying degree of freedom with regardto optimal collimation. Greater flexibility in collimation provides theadvantages of being able to reduce the dose of spatial ionizingradiation to which a patient is exposed while acquiring an x-ray imageof a particular region of the patient's body. However, greaterflexibility in collimation is also accompanied by a complexity inoperating the collimators, which decreases the usability of the overallsystem.

BRIEF SUMMARY OF THE INVENTION

In accordance with various embodiments of the present invention,methods, systems, and apparatus for obtaining a sequence of x-ray imagesare provided herein. Embodiments of the present invention utilizecomputer vision techniques to identify an object of interest, such as aneedle, predict the location of the object of interest in the next imageacquisition and autonomously spatially modify the x-ray to image thepredicted location. By altering the x-ray lightbeam to specificallyimage for the predicted location of the object of interest, theembodiments of the present invention reduce the dose of spatial ionizingradiation that the patient is exposed to without the aforementioneddisadvantages of conventional systems.

In one embodiment, an object of interest is detected in a first x-rayimage and an area of interest is determined based on a predicted motionof the object of interest. A second x-ray image of the area of interestis acquired using spatial x-ray modification to control an x-ray to passthrough a portion of a patient corresponding to the area of interest.

These and other advantages of the present disclosure will be apparent tothose of ordinary skill in the art by reference to the followingDetailed Description and the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart illustrating a method for obtaining a sequence ofx-ray images in accordance with an embodiment of the present invention;

FIG. 2 shows an object of interest detected in an initial x-ray imageand a guidance path of the object of interest in accordance with anembodiment of the present invention;

FIG. 3 shows an area of interest based on a predicted motion of anobject of interest in accordance with an embodiment of the presentinvention;

FIG. 4 shows a next x-ray image of an area of interest in accordancewith an embodiment of the present invention;

FIG. 5 shows an object of interest detected in a next x-ray image of anarea of interest in accordance with an embodiment of the presentinvention;

FIG. 6A shows a diagram of photons scattered beyond a collimated areadefined for an x-ray image in accordance with an embodiment of thepresent invention.

FIG. 6B shows an x-ray scatter image in accordance with an embodiment ofthe present invention;

FIG. 7 is a high-level block diagram of a computer capable ofimplementing the embodiments disclosed herein.

FIG. 8 provides exemplary x-ray images captured via spatial x-raymodification in accordance with an embodiment of the present invention.

DETAILED DESCRIPTION

The present invention is directed to a method and system for obtaining asequence of x-ray images. Embodiments of the present invention aredescribed herein to give a visual understanding of the x-ray sequenceacquisition method. A digital image is often composed of digitalrepresentations of one or more objects (or shapes). The digitalrepresentation of an object is often described herein in terms ofidentifying and manipulating the objects. Such manipulations are virtualmanipulations accomplished in the memory or other circuitry/hardware ofa computer system. Accordingly, it is to be understood that embodimentsof the present invention may be performed within a computer system usingdata stored within the computer system.

FIG. 1 is a flowchart illustrating a method 100 for obtaining a sequenceof images in accordance with an embodiment of the present invention.FIG. 2 shows an object of interest detected in an exemplary x-ray imageand a guidance path of the object of interest in accordance with anembodiment of the present invention.

At step 102, an initial x-ray image is acquired. The initial x-ray imagecan be acquired as one of a sequence of fluoroscopic images. Forexample, the initial x-ray image can be acquired in real-time as part ofa sequence of fluoroscopic images used for needle guidance in a medicalprocedure. The initial x-ray image can be acquired using an x-rayscanning device, such as a C-arm x-ray image acquisition device. Asillustrated in FIG. 2, an x-ray image 202 of a particular portion of apatient's anatomy is acquired.

At step 104, an object of interest 204 is detected in an initial x-rayimage 202. The object of interest 204 can be defined as a specific tool,a region of anatomy or any target region in the x-ray image 202. Asshown in FIG. 2, the object of interest 204 is a needle within ananatomical region of a patient's body.

According to an advantageous embodiment, a learning based method can beused for autonomous detection of the object of interest 204, or, forpurposes of discussion, a needle in the x-ray image 202. Such autonomousdetection can identify the location and orientation of the object ofinterest 202 in the x-ray image 202. When the object of interest 202 isa needle, it can move in a three-dimensional space with six degrees offreedom. The needle's location and orientation in the x-ray image 202 isdefined by two points (x,y) in the x-ray image 202. Although the needleis free to move with six degrees of freedom, only the locationinformation is required for spatial x-ray modification. In an embodimentof the present invention, prior knowledge of the needle's position canalso be used to reduce search space and false positive detection.

The needle is a high contrast object and may have a varied visualappearance with respect to varying imaging devices and imaging deviceparameterization. Learning based methods can be trained to be robust tonoise and capable of handling large variations in appearance. Suchlearning based methods are trained on a set of example data. Thetraining data can be manually annotated or synthetically generated data.The manually annotated training data contains a wide variety of needleorientations and locations within an x-ray image. Synthetic data isgenerated in the form of Digitally Reconstructed Radiograph (DRR). Raysare traced through a computed tomography (CT) scan of a needle togenerate synthetic projection images. The training data can also includereal and synthetically generated examples of the appearance of a needleoccluded by collimators. In addition, the training data includes imageswithout a needle to enable proper classification of non-object regions.

To handle the needle's large variations in appearance, a probabilisticboosting tree (PBT) can be implemented for needle detection. The PBT istrained on a set of example training data in order to learn needlefeatures. The training process for the PBT generates a decision treewhich contains a number of tests based on needle features. The trainingdata is divided into sub-trees within the PBT decision tree according tovarious needle features. The leaf nodes of the resulting PBT decisiontree thereby contain a probabilistic distribution of classes of object(i.e. needle) regions of an x-ray image and non-object regions of anx-ray image.

At runtime, prior to classifying an image patch of the image 202 todetect the object of interest 204, the number of false positives can bereduced by applying a steerable filter to the x-ray image 202 in orderto identify regions of high contrast, which results in a reduction ofthe search space as well as increasing computational performance. Imagefeatures, such as Haar features, are extracted from image templatesidentified by the steerable features. Such features are fed into the PBTto classify the image patch as either being in the object of interest204 or not the object of interest. The 2D position of the needle in thefluoroscopic image is estimated using the classified image patches.

Also, at runtime, prior to classifying an image patch of the image 202to detect the object of interest 204, a low probability region of thex-ray image 202 can be determined based on information related to aguidance path 206 of the object of interest 204. The low-probabilityregion can be removed from a search space of the PBT. In addition,information about the location of the collimators and semi-transparentcollimators can be incorporated as prior knowledge to further reduce thesearch space and increase performance.

In order to detect the object of interest 204 in the x-ray image 202,Haar features are extracted from image patches of the x-ray image 202and the PBT determines a probability score for each image patch. Theimage patch having the highest probability score is determined to be theposition of the object of interest 204 in the x-ray image 202.

In alternative embodiments, object detection may be performed accordingto (1) user input via a computer console, (2) user input via a deviceattached to an external surface of a patient and visible in x-ray and(3) by a user's eye gaze.

Returning to FIG. 1, at step 106, an area of interest is determinedbased on a predicted motion of the object of interest. FIG. 3 shows anarea of interest 302 detected in the x-ray image 202.

When the object of interest 204 (e.g. needle) is static and the imagingdevice is static, an area of interest surrounding the needle will beeasy to define and can be done manually. However, if either the needleor imaging device are in motion, the size of the area of the interestmust account for uncertainty caused by the motion.

When the needle is moving in the x-ray image 202, a next location of theneedle can be predicted by predicting the needle's motion. Predictingthe needle's motion allows for minimizing a size of the area of interest302, which is defined as a region surrounding the predicted nextlocation of the needle. By minimizing the size of the area of interest302 through motion prediction, a minimal dose of ionized radiation willbe required for an x-ray image of the area of interest 302.

In one possible implementation, the needle's motion can be predicted ina 2D fluoroscopic image using any of the following methods: the ExtendedKalman Filter model, a Particle Filter model (or its variants), or aLearnt Motion models. In addition, these motion models can incorporateprior information from a needle path planning phase to reduceuncertainty of the needle's motion. Additionally, it is also feasible toalter the frequency at which x-ray images are captured, such that x-rayimages are captured at a higher rate for an object of interest that ismoving at a rapid pace.

In acquisition of a 3D Dyna CT volume, a set of two-dimensional (2D)x-ray images is taken in order to reconstruct the scanned volume. Thisis usually performed in a rotation that covers 180 degrees, plus a fanangle, because every voxel reconstructed inside the scanned volume hasto be observed from at least 180 degrees. If collimation is performed,the volume that can be reconstructed is also reduced. As the motion ofthe arm and the intrinsic parameters of the imaging device are typicallyknown, the uncertainty in the imaged area is less.

In another possible implementation, the area of interest may also beestimated in 3D by using multiple geometry techniques and epipolarconstraints in conjunction with input from robotic systems associatedwith control of the imaging device. Needle positions in previousprojections describe the position of the needle in a two-dimensional(2D) format. As each view is calibrated, each respective 2D needleposition defines a cone with the source at the tip and the area on thedetector as the base. An intersection of multiple such cones describesan approximate 3D position of the needle. The approximate 3D positionbecomes further refined by each additional projection, and adaptivecontrol is regularized in such a way that at least the approximate 3Dposition of the needle and a small area around it can be reconstructedas the area of interest.

Returning to FIG. 1, at step 108, a next x-ray image 402 of the area ofinterest 302 is acquired using spatial x-ray modification to control anx-ray to pass through a portion of a patient corresponding to the areaof interest 302. FIG. 4 shows the next x-ray image 402 of the area ofinterest 302 in accordance with an embodiment of the present invention.The next x-ray image 402 of the area of interest 302 is shown in FIG. 4relative to the boundaries 202-1 of the initial x-ray image 202 in orderto demonstrate that the size of the next x-ray image 402 of the area ofinterest 302 is significantly smaller than the size of the initial x-rayimage 202, thereby requiring a smaller dose of ionized radiation.

Spatial x-ray modification is the process of controlling the directionin which the x-ray lightbeam will travel such that the x-ray lightbeamwill only pass through the portion of a patient corresponding to thearea of interest 302. One approach for controlling the direction of thex-ray lightbeam is to alter a shape and/or a position of the x-raylightbeam by changing a collimator of an imaging device. Anotherapproach involves the use of one or more semitransparent collimators ofthe imaging device to constrain the x-ray lightbeam's direction(s). Inyet another approach, an angulation of a C-arm of the imaging deviceand/or a rotation of a detector of the imaging device can be changed toinfluence the x-ray lightbeam's direction. In yet another approach, aposition of the table upon which the patient rests, can be changes tofurther control the direction of the x-ray lightbeam. These approachescan be used individually or in any combination to implement the spatialx-ray modification. It is understood that these approaches are notlimiting and other approaches for spatial x-ray modification may be usedas well.

FIG. 8 provides exemplary x-ray images 802, 804, 806, 808 captured viathe various approaches to spatial x-ray modification mentioned above.X-ray image 802 is an x-ray image resulting from altering a shape and/ora position of an x-ray lightbeam by changing a collimator of an imagingdevice. X-ray image 804 is an x-ray image resulting from the use ofthree semitransparent collimators of an imaging device to constrain ax-ray lightbeam's direction(s). X-ray image 806 is an x-ray imageresulting from changing an angulation of a C-arm of the imaging devicein order to influence the x-ray lightbeam's direction. Such an approachmay be applied during acquisition imaging for the process ofreconstruction volumes such as DynaCT. This results in a scan that hasan object-dependent collimation in each view. The collimation isadaptively reconfigured throughout the complete scan. One inputparameter for the adaptive control is obtained from the image by needledetection. However, this may result in an acquisition that does notfulfill the data completeness condition that each voxel that should bereconstructed at least 180 degrees of angular views have to be acquired.X-ray image 808 is an x-ray image resulting from changing a position ofthe table upon which the patient rests.

Returning to FIG. 1, at step 110, the object of interest is detected inthe next x-ray image 402 of the area of interest 302. FIG. 5 illustratesthe object of interest 502 detected in a next x-ray image 402.The objectof interest 502 can be detected in the next x-ray image 402 using alearning based object detection approach described above with respectstep 104.

At step 111, it is determined if the object of interest 502 issuccessfully detected in the next x-ray image 402. If it is determinedthat the object of interest 502 was successfully detected, the method100 returns to step 106, and an area of interest is defined based on thepredicted motion of the object of interest 502.

However, if it is determined at step 111 that the object of interest 502is not successfully detected in the next x-ray image 402, the methodproceeds to step 112. At step 112, the object of interest 502 isdetected in an x-ray scatter image. FIG. 6A shows a diagram of photonsscattered beyond a collimated area defined for an x-ray image. FIG. 6Bshows an exemplary x-ray scatter image 602 of an area beyond acollimated area defined for an x-ray image.

As the object of interest can be an object of high contrast, it isvisible in an x-ray scatter image 602 due to photons that are scatteredby collimator edges. The position of the collimator 606 defines acollimated area for an irradiated cone 608 from an x-ray source 604 topass through an object 612 (such as a patient's body). Althoughcollimation should cause all x-ray photons to be absorbed, the edges ofthe collimator 606 are themselves a source of scattered x-ray photons610. In other words, x-ray photons that hit the collimator edges arescattered beyond the collimated area defined for the x-ray image. Anx-ray scatter image 602 is an image of the scattered photons, andobjects that are in an area covered by the collimator 606 may be visiblein the x-ray scatter image 602. An x-ray scatter image 602 associatedwith the x-ray image is generated in order to be used in detection ofthe object of interest.

Since the signal intensity and also the appearance of the object ofinterest is somewhat different in the x-ray scatter image 602 than in afull-beam x-ray image, an adapted version of the needle detectionalgorithm described above uses different training data matched to thex-ray scatter image data. Specifically, the object of interest can bedetected in the x-ray scatter image 602 using a PBT trained on annotatedtraining data including x-ray scatter image data.

Once the object is detected using the x-ray scatter image 602, themethod, as illustrated in flowchart 100 of FIG. 1, returns to steps106-112 and steps 106-112 are then repeated resulting in a sequence ofx-ray images being acquired. It is understood that steps 106-112 can berepeated any number of times depending on how large of a sequence x-rayimages is required. These steps can be repeated in real-time during asurgical procedure to provide needle guidance.

The above-described methods for obtaining a sequence of x-ray images maybe implemented on a computer using well-known computer processors,memory units, storage devices, computer software, and other components.A high-level block diagram of such a computer is illustrated in FIG. 7.Computer 702 contains a processor 704, which controls the overalloperation of the computer 702 by executing computer program instructionswhich define such operation. The computer program instructions may bestored in a storage device 712 (e.g., magnetic disk) and loaded intomemory 710 when execution of the computer program instructions isdesired. Thus, the steps of the methods of FIG. 1 may be defined by thecomputer program instructions stored in the memory 710 and/or storage712 and controlled by the processor 704 executing the computer programinstructions. An imaging device 720, such as a C-arm image acquisitiondevice, can be connected to the computer 702 to input image data to thecomputer 702. It is possible to implement the imaging device 720 and thecomputer 702 as one device. It is also possible that the imaging device720 and the computer 702 communicate wirelessly through a network. Thecomputer 702 also includes one or more network interfaces 706 forcommunicating with other devices via a network. The computer 702 alsoincludes other input/output devices 708 that enable user interactionwith the computer 702 (e.g., display, keyboard, mouse, speakers,buttons, etc.). One skilled in the art will recognize that animplementation of an actual computer could contain other components aswell, and that FIG. 7 is a high level representation of some of thecomponents of such a computer for illustrative purposes.

The foregoing Detailed Description is to be understood as being in everyrespect illustrative and exemplary, but not restrictive, and the scopeof the invention disclosed herein is not to be determined from theDetailed Description, but rather from the claims as interpretedaccording to the full breadth permitted by the patent laws. It is to beunderstood that the embodiments shown and described herein are onlyillustrative of the principles of the present invention and that variousmodifications may be implemented by those skilled in the art withoutdeparting from the scope and spirit of the invention. Those skilled inthe art could implement various other feature combinations withoutdeparting from the scope and spirit of the invention.

What is claimed is:
 1. A method for obtaining a sequence of x-rayimages, comprising: detecting an object of interest in a first x-rayimage; determining an area of interest based on a predicted motion ofthe object of interest; and acquiring a second x-ray image of the areaof interest using spatial x-ray modification to control an x-ray to passthrough a portion of a patient corresponding to the area of interest. 2.The method of claim 1, wherein detecting an object of interest in afirst x-ray image comprises: automatically detecting the object ofinterest in the first x-ray image using a probabilistic boosting tree(PBT) trained on annotated training data.
 3. The method of claim 2,wherein detecting an object of interest in a first x-ray image furthercomprises: determining at least one low probability region of the firstx-ray image based on information related to a guidance path of theobject of interest; and removing the determined at least one lowprobability region from a search space of the PBT prior to automaticallydetecting the object of interest in the first x-ray image using the PBT.4. The method of claim 2, wherein detecting an object of interest in afirst x-ray image further comprises: applying a steerable filter to thefirst x-ray image to identify at least one high contrast region of thefirst x-ray image; and removing the identified at least one highcontrast region from a search space of the PBT prior to automaticallydetecting the object of interest in the first x-ray image using the PBT.5. The method of claim 1, wherein determining an area of interest basedon a predicted motion of the object of interest comprises: predicting anext location of the object of interest by predicting a motion of theobject of interest; and defining the area of interest as a regionsurrounding the predicted next location of the object of interest. 6.The method of claim 5, wherein predicting a next location of the objectof interest by predicting a motion of the object if interest comprises:predicting the motion of the object of interest using one of: anExtended Kalman Filter model, a Particle Filter model, and a LearntMotion model.
 7. The method of claim 6, wherein the one of the ExtendedKalman Filter model, the Particle Filter model, and the Learnt Motionmodel incorporates prior information regarding a path of the object ofinterest.
 8. The method of claim 1, wherein acquiring a second x-rayimage of the area of interest using spatial x-ray modification tocontrol an x-ray to pass through a portion of a patient corresponding tothe area of interest comprises: changing at least one collimator of animaging device to alter at least one of a shape and a position of anx-ray lightbeam, such that the x-ray lightbeam only passes through theportion of the patient corresponding to the area of interest.
 9. Themethod of claim 1, wherein acquiring a second x-ray image of the area ofinterest comprises : utilizing at least one semitransparent collimatorof the image device to constrain the x-ray lightbeam to only passthrough the portion of the patient corresponding to the area ofinterest.
 10. The method of claim 1, wherein acquiring a second x-rayimage of the area of interest comprises: changing at least one of anangulation of a C-arm of the imaging device and a rotation of a detectorof the imaging device.
 11. The method of claim 1, wherein acquiring asecond x-ray image of the area of interest comprises: changing aposition of a table of the imaging device.
 12. The method of claim 1,further comprising: detecting the object of interest in the second x-rayimage; determining a second area of interest based on a second predictedmotion of the object of interest detected in the second x-ray image;andacquiring a third x-ray image of the second area of interest usingspatial x-ray modification to control an x-ray to pass through a portionof the patient corresponding to the second area of interest.
 13. Themethod of claim 1, further comprising: detecting the object of interestin the second x-ray image; determining if the object of interest wassuccessfully detected in the second x-ray image; and if the object ofinterest was not successfully detected in the second x-ray image:generating an x-ray scatter image associated with the second x-rayimage; and detecting the object of interest in the x-ray scatter image.14. The method of claim 13, wherein detecting the object of interest inthe x-ray scatter image comprises: detecting the object of interest inthe x-ray scatter image using a probabilistic boosting tree (PBT)trained on annotated training data comprising x-ray scatter image data.15. A non-transitory computer readable medium storing computer programinstructions for obtaining a sequence of x-ray images, which, when theinstructions are executed on a processor, cause the processor to performoperations comprising: detecting an object of interest in a first x-rayimage; determining an area of interest based on a predicted motion ofthe object of interest; and acquiring a second x-ray image of the areaof interest using spatial x-ray modification to control an x-ray to passthrough a portion of a patient corresponding to the area of interest.16. The non-transitory computer readable medium of claim 15, whereindetecting an object of interest in a first x-ray image comprises:automatically detecting the object of interest in the first x-ray imageusing a probabilistic boosting tree (PBT) trained on annotated trainingdata.
 17. The non-transitory computer readable medium of claim 16,wherein detecting an object of interest in a first x-ray image furthercomprises: determining at least one low probability region of the firstx-ray image based on information related to a guidance path of theobject of interest; and removing the determined at least one lowprobability region from a search space of the PBT prior to automaticallydetecting the object of interest in the first x-ray image using the PBT.18. The non-transitory computer readable medium of claim 16, whereindetecting an object of interest in a first x-ray image furthercomprises: applying a steerable filter to the first x-ray image toidentify at least one high contrast region of the first x-ray image; andremoving the identified at least one high contrast region from a searchspace of the PBT prior to automatically detecting the object of interestin the first x-ray image using the PBT.
 19. The non-transitory computerreadable medium of claim 15, wherein determining an area of interestbased on a predicted motion of the object of interest comprises:predicting a next location of the object of interest by predicting amotion of the object of interest; and defining the area of interest as aregion surrounding the predicted next location of the object ofinterest.
 20. The non-transitory computer readable medium of claim 19,wherein predicting a next location of the object of interest bypredicting a motion of the object if interest comprises: predicting themotion of the object of interest using one of: an Extended Kalman Filtermodel, a Particle Filter model, and a Learnt Motion model.
 21. Thenon-transitory computer readable medium of claim 20, wherein the one ofthe Extended Kalman Filter model, the Particle Filter model, and theLearnt Motion model incorporates prior information regarding a path ofthe object of interest.
 22. The non-transitory computer readable mediumof claim 15, wherein acquiring a second x-ray image of the area ofinterest using spatial x-ray modification to control an x-ray to passthrough a portion of a patient corresponding to the area of interestcomprises: changing at least one collimator of an imaging device toalter at least one of a shape and a position of an x-ray lightbeam, suchthat the x-ray lightbeam only passes through the portion of the patientcorresponding to the area of interest.
 23. The non-transitory computerreadable medium of claim 15, wherein acquiring a second x-ray image ofthe area of interest comprises: utilizing at least one semitransparentcollimator of the image device to constrain the x-ray lightbeam to onlypass through the portion of the patient corresponding to the area ofinterest.
 24. The non-transitory computer readable medium of claim 15,wherein acquiring a second x-ray image of the area of interestcomprises: changing at least one of an angulation of a C-arm of theimaging device of the imaging device and a rotation of a detector of theimaging device.
 25. The non-transitory computer readable medium of claim15, wherein acquiring a second x-ray image of the area of interestcomprises: changing a position of a table of the imaging device.
 26. Thenon-transitory computer readable medium of claim 15, further comprising:detecting the object of interest in the second x-ray image; determininga second area of interest based on a second predicted motion of theobject of interest detected in the second x-ray image; and acquiring athird x-ray image of the second area of interest using spatial x-raymodification to control an x-ray to pass through a portion of thepatient corresponding to the second area of interest.
 27. Thenon-transitory computer readable medium of claim 15, wherein detectingthe object of interest in the second x-ray image comprises: detectingthe object of interest in the second x-ray image using the first PBT;determining if the object of interest was successfully detected in thesecond x-ray image; and if the object of interest was not successfullydetected in the second x-ray image: generating an x-ray scatter imageassociated with the second x-ray image; and detecting the object ofinterest in the x-ray scatter image.
 28. The non-transitory computerreadable medium of claim 27, wherein detecting the object of interest inthe x-ray scatter image comprises: detecting the object of interest inthe x-ray scatter image using a probabilistic boosting tree (PBT)trained on annotated training data comprising x-ray scatter image data.29. A system for obtaining a sequence of x-ray images, comprising: meansfor detecting an object of interest in a first x-ray image; means fordetermining an area of interest based on a predicted motion of theobject of interest; and means for acquiring a second x-ray image of thearea of interest using spatial x-ray modification to control an x-ray topass through a portion of a patient corresponding to the area ofinterest.
 30. The system of claim 29, wherein the means for determiningan area of interest based on a predicted motion of the object ofinterest comprises: means for predicting a next location of the objectof interest by predicting a motion of the object of interest; and meansfor defining the area of interest as a region surrounding the predictednext location of the object of interest.
 31. The system of claim 29,wherein the means for detecting an object of interest in a first x-rayimage comprises: means for automatically detecting the object ofinterest in the first x-ray image using a probabilistic boosting tree(PBT) trained on annotated training data.
 32. The system of claim 29,further comprising: means for detecting the object of interest in thesecond x-ray image; means for determining a second area of interestbased on a second predicted motion of the object of interest detected inthe second x-ray image; and means for acquiring a third x-ray image ofthe second area of interest using spatial x-ray modification to controlan x-ray to pass through a portion of the patient corresponding to thesecond area of interest.
 33. The system of claim 29, further comprising:means for detecting the object of interest in the second x-ray image;means for determining if the object of interest was successfullydetected in the second x-ray image; and if the object of interest wasnot successfully detected in the second x-ray image: means forgenerating an x-ray scatter image associated with the second x-rayimage; and means for detecting the object of interest in the x-rayscatter image.
 34. The system of claim 33, wherein the means fordetecting the object of interest in the x-ray scatter image comprises:means for detecting the object of interest in the x-ray scatter imageusing a probabilistic boosting tree (PBT) trained on annotated trainingdata comprising x-ray scatter image data.